Adversarial Robustness for Machine Learning Models

While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems. Adversarial robustness has become one of the mainstream topics in machine learning with much research carried out, while many companies have started to incorporate security and robustness into their systems. Adversarial Robustness for Machine Learning Models summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense, and veri?cation. It contains 6 parts: The ?rst three parts cover adversarial attack, veri?cation, and defense, mainly focusing on image classi?cation applications, which is the standard benchmark considered in the adversarial robustness community. It then discusses adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. For researchers, this book provides a thorough literature review that summarizes latest progress in this area, which can be a good reference for conducting future research. It could also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning. Summarizes the whole field of adversarial robustness for Machine learning models A clearly explained, self-contained reference Introduces formulations, algorithms and intuitions Includes applications based on adversarial robustness

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  • Author : Pin-Yu Chen
  • Publisher : Academic Press
  • Pages : 425 pages
  • ISBN : 9780128240205
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKAdversarial Robustness for Machine Learning Models

Adversarial Robustness for Machine Learning Models

Adversarial Robustness for Machine Learning Models
  • Author : Pin-Yu Chen,Cho-Jui Hsieh
  • Publisher : Academic Press
  • Release : 15 September 2022
GET THIS BOOKAdversarial Robustness for Machine Learning Models

While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems. Adversarial robustness has become one of the mainstream topics in machine learning with much research carried out, while many companies have started to incorporate security and robustness into their systems. Adversarial Robustness for Machine Learning

Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies

Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies
  • Author : National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Computer Science and Telecommunications Board,Board on Mathematical Sciences and Analytics,Intelligence Community Studies Board
  • Publisher : National Academies Press
  • Release : 22 August 2019
GET THIS BOOKRobust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies

The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11â€"12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.

Enhancing Adversarial Robustness of Deep Neural Networks

Enhancing Adversarial Robustness of Deep Neural Networks
  • Author : Jeffrey Zhang (M. Eng.)
  • Publisher : Unknown Publisher
  • Release : 30 November 2021
GET THIS BOOKEnhancing Adversarial Robustness of Deep Neural Networks

Logit-based regularization and pretrain-then-tune are two approaches that have recently been shown to enhance adversarial robustness of machine learning models. In the realm of regularization, Zhang et al. (2019) proposed TRADES, a logit-based regularization optimization function that has been shown to improve upon the robust optimization framework developed by Madry et al. (2018) [14, 9]. They were able to achieve state-of-the-art adversarial accuracy on CIFAR10. In the realm of pretrain- then-tune models, Hendrycks el al. (2019) demonstrated that adversarially pretraining a model on ImageNet then

Adversarial Machine Learning

Adversarial Machine Learning
  • Author : Anthony D. Joseph,Blaine Nelson,Benjamin I. P. Rubinstein,J. D. Tygar
  • Publisher : Cambridge University Press
  • Release : 30 April 2018
GET THIS BOOKAdversarial Machine Learning

This study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.

Adversarial Machine Learning

Adversarial Machine Learning
  • Author : Yevgeniy Vorobeychik,Murat Kantarcioglu
  • Publisher : Morgan & Claypool Publishers
  • Release : 08 August 2018
GET THIS BOOKAdversarial Machine Learning

The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at

Deep Learning: Algorithms and Applications

Deep Learning: Algorithms and Applications
  • Author : Witold Pedrycz,Shyi-Ming Chen
  • Publisher : Springer Nature
  • Release : 23 October 2019
GET THIS BOOKDeep Learning: Algorithms and Applications

This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids,

Artificial Neural Networks and Machine Learning – ICANN 2021

Artificial Neural Networks and Machine Learning – ICANN 2021
  • Author : Igor Farkaš,Paolo Masulli,Sebastian Otte,Stefan Wermter
  • Publisher : Springer Nature
  • Release : 11 September 2021
GET THIS BOOKArtificial Neural Networks and Machine Learning – ICANN 2021

The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as adversarial machine learning, anomaly detection, attention and transformers, audio and multimodal applications, bioinformatics and biosignal analysis, capsule networks and cognitive models. *The

Robust Machine Learning in Adversarial Setting with Provable Guarantee

Robust Machine Learning in Adversarial Setting with Provable Guarantee
  • Author : Yizhen Wang
  • Publisher : Unknown Publisher
  • Release : 30 November 2021
GET THIS BOOKRobust Machine Learning in Adversarial Setting with Provable Guarantee

Over the last decade, machine learning systems have achieved state-of-the-art performance in many fields, and are now used in increasing number of applications. However, recent research work has revealed multiple attacks to machine learning systems that significantly reduce the performance by manipulating the training or test data. As machine learning is increasingly involved in high-stake decision making processes, the robustness of machine learning systems in adversarial environment becomes a major concern. This dissertation attempts to build machine learning systems robust

Intelligent Systems and Applications

Intelligent Systems and Applications
  • Author : Kohei Arai,Supriya Kapoor,Rahul Bhatia
  • Publisher : Springer Nature
  • Release : 30 November 2021
GET THIS BOOKIntelligent Systems and Applications

The book Intelligent Systems and Applications - Proceedings of the 2020 Intelligent Systems Conference is a remarkable collection of chapters covering a wider range of topics in areas of intelligent systems and artificial intelligence and their applications to the real world. The Conference attracted a total of 545 submissions from many academic pioneering researchers, scientists, industrial engineers, students from all around the world. These submissions underwent a double-blind peer review process. Of those 545 submissions, 177 submissions have been selected to be included in

Machine Learning with Provable Robustness Guarantees

Machine Learning with Provable Robustness Guarantees
  • Author : Huan Zhang
  • Publisher : Unknown Publisher
  • Release : 30 November 2021
GET THIS BOOKMachine Learning with Provable Robustness Guarantees

Although machine learning has achieved great success in numerous complicated tasks, many machine learning models lack robustness under the presence of adversaries and can be misled by imperceptible adversarial noises. In this dissertation, we first study the robustness verification problem of machine learning, which gives provable guarantees on worst case performance under arbitrarily strong adversaries. We study two popular machine learning models, deep neural networks (DNNs) and ensemble trees, and design efficient and effective algorithms to provably verify the robustness

Strengthening Deep Neural Networks

Strengthening Deep Neural Networks
  • Author : Katy Warr
  • Publisher : "O'Reilly Media, Inc."
  • Release : 03 July 2019
GET THIS BOOKStrengthening Deep Neural Networks

As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re

On the Robustness of Neural Network: Attacks and Defenses

On the Robustness of Neural Network: Attacks and Defenses
  • Author : Minhao Cheng
  • Publisher : Unknown Publisher
  • Release : 30 November 2021
GET THIS BOOKOn the Robustness of Neural Network: Attacks and Defenses

Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples. That is, a slightly modified example could be easily generated and fool a well-trained image classifier based on deep neural networks (DNNs) with high confidence. This makes it difficult to apply neural networks in security-critical areas. To find such examples, we first introduce and define adversarial examples. In the first part, we then discuss how to build adversarial attacks in both image

Robust Machine Learning Models and Their Applications

Robust Machine Learning Models and Their Applications
  • Author : Hongge Chen (Ph. D.)
  • Publisher : Unknown Publisher
  • Release : 30 November 2021
GET THIS BOOKRobust Machine Learning Models and Their Applications

Recent studies have demonstrated that machine learning models are vulnerable to adversarial perturbations – a small and human-imperceptible input perturbation can easily change the model output completely. This has created serious security threats to many real applications, so it becomes important to formally verify the robustness of machine learning models. This thesis studies the robustness of deep neural networks as well as tree-based models, and considers the applications of robust machine learning models in deep reinforcement learning. We first develop a

Perturbations, Optimization, and Statistics

Perturbations, Optimization, and Statistics
  • Author : Tamir Hazan,George Papandreou,Daniel Tarlow
  • Publisher : MIT Press
  • Release : 23 December 2016
GET THIS BOOKPerturbations, Optimization, and Statistics

A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules

Engineering Dependable and Secure Machine Learning Systems

Engineering Dependable and Secure Machine Learning Systems
  • Author : Onn Shehory,Eitan Farchi,Guy Barash
  • Publisher : Springer Nature
  • Release : 07 November 2020
GET THIS BOOKEngineering Dependable and Secure Machine Learning Systems

This book constitutes the revised selected papers of the Third International Workshop on Engineering Dependable and Secure Machine Learning Systems, EDSMLS 2020, held in New York City, NY, USA, in February 2020. The 7 full papers and 3 short papers were thoroughly reviewed and selected from 16 submissions. The volume presents original research on dependability and quality assurance of ML software systems, adversarial attacks on ML software systems, adversarial ML and software engineering, etc.